Journal
IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
Volume 14, Issue 4, Pages 1255-1266Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSA.2005.860835
Keywords
covariance matrices; density functions; EM algorithm; factor analysis; Gaussian mixture models (GMMs); speech recognition
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We introduce a class of Gaussian mixture models (GMMs) in which the covariances or the precisions (inverse co-variances) are restricted to lie in subspaces spanned by rank-one symmetric matrices. The rank-one basis are shared between the Gaussians according to a sharing structure. We describe an algorithm for estimating the parameters of the GMM in a maximum likelihood framework given a sharing structure. We employ these models for modeling the observations in the hidden-states of a hidden Markov model based speech recognition system. We show that this class of models provide improvement in accuracy and computational efficiency over well-known covariance modeling techniques such as classical factor analysis, shared factor analysis and maximum likelihood linear transformation based models which are special instances of this class of models. We also investigate different sharing mechanisms. We show that for the same number of parameters, modeling precisions leads to better performance when compared to modeling covariances. Modeling precisions also gives a distinct advantage in computational and memory requirements.
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